Jonathan Binas

PhD Student
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I was trained as a physicist at ETH Zurich before joining INI for a PhD. I have always been interested in the interdisciplinary field of neural computation, where biology, physics, mathematics, engineering, and computer science meet.

I work on neuro-inspired models of computation and computing architectures, both theoretically and by developing new kinds of computing hardware.

Specifically, my work focuses on

> Ultra-efficient deep neural network implementations based on analog VLSI technology,
> Event-based and probabilistic computation in recurrent neural assemblies,
> Spike-based neural computation,
> Learning and self-organization of neural circuitry.



  • Binas, J. and Indiveri, G. and and Pfeiffer, M. Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers, International Symposium on Circuits and Systems (ISCAS) 2094--2097, 2016 pdf


  • Binas, J. and Indiveri, G. and Pfeiffer, M. Local Structure Helps Learning Optimized Automata in Recurrent Neural Networks, IEEE International Joint Conference on Neural Networks (IJCNN), 2015 pdf
  • Diehl, P.U. and Neil, D. and Binas, J. and Cook, M. and Liu, S.C. and Pfeiffer, M. Fast-Classifying, High-Accuracy Spiking Deep Networks Through Weight and Threshold Balancing, IEEE International Joint Conference on Neural Networks (IJCNN), 2015 pdf




  • Neftci, Emre and Binas, Jonathan and Chicca, Elisabetta and Indiveri, Giacomo and Douglas, Rodney Systematic Construction of Finite State Automata Using VLSI Spiking Neurons, Biomimetic and Biohybrid Systems 382-383, 2012
© 2016 Institut für Neuroinformatik